{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n\n<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "In this notebook we try to practice all the classification algorithms that we learned in this course.\n\nWe load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n\nLets first load required libraries:"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [],
            "source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom sklearn import preprocessing\n%matplotlib inline"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### About dataset"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n\n| Field          | Description                                                                           |\n|----------------|---------------------------------------------------------------------------------------|\n| Loan_status    | Whether a loan is paid off on in collection                                           |\n| Principal      | Basic principal loan amount at the                                                    |\n| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n| Effective_date | When the loan got originated and took effects                                         |\n| Due_date       | Since it\u2019s one-time payoff schedule, each loan has one single due date                |\n| Age            | Age of applicant                                                                      |\n| Education      | Education of applicant                                                                |\n| Gender         | The gender of applicant                                                               |"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets download the dataset"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-01-02 14:18:17--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23101 (23K) [text/csv]\nSaving to: \u2018loan_train.csv\u2019\n\nloan_train.csv      100%[===================>]  22.56K  19.9KB/s    in 1.1s    \n\n2020-01-02 14:18:23 (19.9 KB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
                }
            ],
            "source": "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Load Data From CSV File  "
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>9/8/2016</td>\n      <td>9/22/2016</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/9/2016</td>\n      <td>10/8/2016</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30       9/8/2016   \n1           2             2     PAIDOFF       1000     30       9/8/2016   \n2           3             3     PAIDOFF       1000     15       9/8/2016   \n3           4             4     PAIDOFF       1000     30       9/9/2016   \n4           6             6     PAIDOFF       1000     30       9/9/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   45  High School or Below    male  \n1  10/7/2016   33              Bechalor  female  \n2  9/22/2016   27               college    male  \n3  10/8/2016   28               college  female  \n4  10/8/2016   29               college    male  "
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df = pd.read_csv('loan_train.csv')\ndf.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(346, 10)"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.shape"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Convert to date time object "
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  \n0 2016-10-07   45  High School or Below    male  \n1 2016-10-07   33              Bechalor  female  \n2 2016-09-22   27               college    male  \n3 2016-10-08   28               college  female  \n4 2016-10-08   29               college    male  "
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Data visualization and pre-processing\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Let\u2019s see how many of each class is in our data set "
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "PAIDOFF       260\nCOLLECTION     86\nName: loan_status, dtype: int64"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['loan_status'].value_counts()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "260 people have paid off the loan on time while 86 have gone into collection \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Lets plot some columns to underestand data better:"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Collecting package metadata (repodata.json): done\nSolving environment: done\n\n## Package Plan ##\n\n  environment location: /anaconda3\n\n  added / updated specs:\n    - seaborn\n\n\nThe following packages will be downloaded:\n\n    package                    |            build\n    ---------------------------|-----------------\n    ca-certificates-2019.11.27 |                0         131 KB  anaconda\n    certifi-2019.11.28         |           py37_0         156 KB  anaconda\n    conda-4.8.0                |           py37_1         3.0 MB  anaconda\n    openssl-1.1.1              |       h1de35cc_0         4.6 MB  anaconda\n    seaborn-0.9.0              |     pyh91ea838_1         164 KB  anaconda\n    ------------------------------------------------------------\n                                           Total:         8.1 MB\n\nThe following packages will be UPDATED:\n\n  openssl            conda-forge::openssl-1.1.1d-h0b31af3_0 --> anaconda::openssl-1.1.1-h1de35cc_0\n  seaborn            pkgs/main/osx-64::seaborn-0.9.0-py37_0 --> anaconda/noarch::seaborn-0.9.0-pyh91ea838_1\n\nThe following packages will be SUPERSEDED by a higher-priority channel:\n\n  ca-certificates    conda-forge::ca-certificates-2019.11.~ --> anaconda::ca-certificates-2019.11.27-0\n  certifi                                       conda-forge --> anaconda\n  conda                                         conda-forge --> anaconda\n\n\n\nDownloading and Extracting Packages\nopenssl-1.1.1        | 4.6 MB    | ##################################### | 100% \nseaborn-0.9.0        | 164 KB    | ##################################### | 100% \nca-certificates-2019 | 131 KB    | ##################################### | 100% \nconda-4.8.0          | 3.0 MB    | ##################################### | 100% \ncertifi-2019.11.28   | 156 KB    | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
                }
            ],
            "source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "import seaborn as sns\n\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "bins = np.linspace(df.age.min(), df.age.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'age', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Pre-processing:  Feature selection/extraction"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Lets look at the day of the week people get the loan "
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x216 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "df['dayofweek'] = df['effective_date'].dt.dayofweek\nbins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n      <th>dayofweek</th>\n      <th>weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>male</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>female</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>male</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>female</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>male</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below    male          3        0  \n1 2016-10-07   33              Bechalor  female          3        0  \n2 2016-09-22   27               college    male          3        0  \n3 2016-10-08   28               college  female          4        1  \n4 2016-10-08   29               college    male          4        1  "
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## Convert Categorical features to numerical values"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets look at gender:"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "Gender  loan_status\nfemale  PAIDOFF        0.865385\n        COLLECTION     0.134615\nmale    PAIDOFF        0.731293\n        COLLECTION     0.268707\nName: loan_status, dtype: float64"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "86 % of female pay there loans while only 73 % of males pay there loan\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets convert male to 0 and female to 1:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n      <th>dayofweek</th>\n      <th>weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>45</td>\n      <td>High School or Below</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-08</td>\n      <td>2016-10-07</td>\n      <td>33</td>\n      <td>Bechalor</td>\n      <td>1</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>3</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>15</td>\n      <td>2016-09-08</td>\n      <td>2016-09-22</td>\n      <td>27</td>\n      <td>college</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>4</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>28</td>\n      <td>college</td>\n      <td>1</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>6</td>\n      <td>6</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>2016-09-09</td>\n      <td>2016-10-08</td>\n      <td>29</td>\n      <td>college</td>\n      <td>0</td>\n      <td>4</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           0             0     PAIDOFF       1000     30     2016-09-08   \n1           2             2     PAIDOFF       1000     30     2016-09-08   \n2           3             3     PAIDOFF       1000     15     2016-09-08   \n3           4             4     PAIDOFF       1000     30     2016-09-09   \n4           6             6     PAIDOFF       1000     30     2016-09-09   \n\n    due_date  age             education  Gender  dayofweek  weekend  \n0 2016-10-07   45  High School or Below       0          3        0  \n1 2016-10-07   33              Bechalor       1          3        0  \n2 2016-09-22   27               college       0          3        0  \n3 2016-10-08   28               college       1          4        1  \n4 2016-10-08   29               college       0          4        1  "
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## One Hot Encoding  \n#### How about education?"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "education             loan_status\nBechalor              PAIDOFF        0.750000\n                      COLLECTION     0.250000\nHigh School or Below  PAIDOFF        0.741722\n                      COLLECTION     0.258278\nMaster or Above       COLLECTION     0.500000\n                      PAIDOFF        0.500000\ncollege               PAIDOFF        0.765101\n                      COLLECTION     0.234899\nName: loan_status, dtype: float64"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "#### Feature befor One Hot Encoding"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>education</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>High School or Below</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>Bechalor</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>college</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>college</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>college</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Principal  terms  age  Gender             education\n0       1000     30   45       0  High School or Below\n1       1000     30   33       1              Bechalor\n2       1000     15   27       0               college\n3       1000     30   28       1               college\n4       1000     30   29       0               college"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df[['Principal','terms','age','Gender','education']].head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame "
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  "
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Feature selection"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Lets defind feature sets, X:"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>age</th>\n      <th>Gender</th>\n      <th>weekend</th>\n      <th>Bechalor</th>\n      <th>High School or Below</th>\n      <th>college</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>45</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>33</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1000</td>\n      <td>15</td>\n      <td>27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>28</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1000</td>\n      <td>30</td>\n      <td>29</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n0       1000     30   45       0        0         0                     1   \n1       1000     30   33       1        0         1                     0   \n2       1000     15   27       0        0         0                     0   \n3       1000     30   28       1        1         0                     0   \n4       1000     30   29       0        1         0                     0   \n\n   college  \n0        0  \n1        0  \n2        1  \n3        1  \n4        1  "
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = Feature\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "What are our lables?"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n      dtype=object)"
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "y = df['loan_status'].values\ny[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "## Normalize Data "
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                },
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n        -0.38170062,  1.13639374, -0.86968108],\n       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n         2.61985426, -0.87997669, -0.86968108],\n       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n        -0.38170062, -0.87997669,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n        -0.38170062, -0.87997669,  1.14984679],\n       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n        -0.38170062, -0.87997669,  1.14984679]])"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Train Test Split"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
                }
            ],
            "source": "from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape,  y_train.shape)\nprint ('Test set:', X_test.shape,  y_test.shape)"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "# Classification "
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\nYou should use the following algorithm:\n- K Nearest Neighbor(KNN)\n- Decision Tree\n- Support Vector Machine\n- Logistic Regression\n\n\n\n__ Notice:__ \n- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n- You should include the code of the algorithm in the following cells."
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# K Nearest Neighbor(KNN)\nNotice: You should find the best k to build the model with the best accuracy.  \n**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set Accuracy:  0.8152173913043478\nTest set Accuracy:  0.6857142857142857\n"
                }
            ],
            "source": "#import library\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics\n\nk = 4\n#Train Model and Predict  \nneigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n\n#fit model and calculate result\nyhat = neigh.predict(X_test)\n\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [],
            "source": "#calculate accuracy for different values of K\nKs = 10\nmean_acc = np.zeros((Ks-1))\nstd_acc = np.zeros((Ks-1))\nConfustionMx = [];\nfor n in range(1,Ks):\n    \n    #Train Model and Predict  \n    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n    yhat=neigh.predict(X_test)\n    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n\n    \n    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "From the graph, we find that the best k is 7"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "plt.plot(range(1,Ks),mean_acc,'g')\nplt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\nplt.legend(('Accuracy ', '+/- 3xstd'))\nplt.ylabel('Accuracy ')\nplt.xlabel('Number of Nabors (K)')\nplt.tight_layout()\nplt.show()"
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set Accuracy:  0.8079710144927537\nTest set Accuracy:  0.7857142857142857\n"
                }
            ],
            "source": "#train another model for k = 7\nk = 7\n#Train Model and Predict  \nneigh2 = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n\n#fit model and calculate result\nyhat2 = neigh2.predict(X_test)\n\nprint(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh2.predict(X_train)))\nprint(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat2))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Decision Tree"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.tree import DecisionTreeClassifier\n#Modelling\nTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 6)\nTree.fit(X_train,y_train)\npredTree = Tree.predict(X_test)"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Accuracy:  0.7714285714285715\n"
                }
            ],
            "source": "#print evaluation\nfrom sklearn import metrics\nimport matplotlib.pyplot as plt\n\nprint(\"Accuracy: \", metrics.accuracy_score(y_test, predTree))"
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.image.AxesImage at 0x1a1faeff60>"
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 7200x14400 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "#draw the graph\nfrom sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline \ndot_data = StringIO()\nfilename = \"loan.png\"\nfeatureNames = df.columns[0:8]\ntargetNames = df['loan_status'].unique().tolist()\nout=tree.export_graphviz(Tree,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y_train), filled=True,  special_characters=True,rotate=False)  \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Support Vector Machine"
        },
        {
            "cell_type": "code",
            "execution_count": 36,
            "metadata": {},
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/anaconda3/lib/python3.7/site-packages/sklearn/svm/base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n  \"avoid this warning.\", FutureWarning)\n"
                }
            ],
            "source": "from sklearn import svm\nclf = svm.SVC(kernel='rbf')\nclf.fit(X_train, y_train) \nyhat_clf = clf.predict(X_test)"
        },
        {
            "cell_type": "code",
            "execution_count": 37,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "              precision    recall  f1-score   support\n\n  COLLECTION       0.36      0.27      0.31        15\n     PAIDOFF       0.81      0.87      0.84        55\n\n    accuracy                           0.74        70\n   macro avg       0.59      0.57      0.57        70\nweighted avg       0.72      0.74      0.73        70\n\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n"
                },
                {
                    "data": {
                        "text/plain": "0.7428571428571429"
                    },
                    "execution_count": 37,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "from sklearn.metrics import classification_report, confusion_matrix\nimport itertools\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\ncnf_matrix = confusion_matrix(y_test, yhat_clf, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat_clf))\n\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False,  title='Confusion matrix')\n\nfrom sklearn.metrics import f1_score\nf1_score(y_test, yhat_clf, average='weighted')\n\nfrom sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat_clf)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Logistic Regression"
        },
        {
            "cell_type": "code",
            "execution_count": 39,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='warn', n_jobs=None, penalty='l2',\n                   random_state=None, solver='liblinear', tol=0.0001, verbose=0,\n                   warm_start=False)"
                    },
                    "execution_count": 39,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "#import libraries\nfrom sklearn import preprocessing\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\n\n#preprocessing\nX = preprocessing.StandardScaler().fit(X).transform(X)\n\n#fit logistic regression model\nLogR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\nLogR"
        },
        {
            "cell_type": "code",
            "execution_count": 40,
            "metadata": {},
            "outputs": [],
            "source": "# get prediction\nyhat = LogR.predict(X_test)\nyhat_prob = LogR.predict_proba(X_test)"
        },
        {
            "cell_type": "code",
            "execution_count": 43,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "jaccard_similarity_score:  0.6857142857142857\nlog loss:  0.5772287609479654\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n"
                }
            ],
            "source": "from sklearn.metrics import jaccard_similarity_score\nprint(\"jaccard_similarity_score: \", jaccard_similarity_score(y_test, yhat))\nfrom sklearn.metrics import log_loss\nprint(\"log loss: \", log_loss(y_test, yhat_prob))"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Model Evaluation using Test set"
        },
        {
            "cell_type": "code",
            "execution_count": 44,
            "metadata": {},
            "outputs": [],
            "source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, download and load the test set:"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "--2020-01-02 14:42:37--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 3642 (3.6K) [text/csv]\nSaving to: \u2018loan_test.csv\u2019\n\nloan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n\n2020-01-02 14:42:40 (69.5 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
                }
            ],
            "source": "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "### Load Test set for evaluation "
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Unnamed: 0</th>\n      <th>Unnamed: 0.1</th>\n      <th>loan_status</th>\n      <th>Principal</th>\n      <th>terms</th>\n      <th>effective_date</th>\n      <th>due_date</th>\n      <th>age</th>\n      <th>education</th>\n      <th>Gender</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/8/2016</td>\n      <td>10/7/2016</td>\n      <td>50</td>\n      <td>Bechalor</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>5</td>\n      <td>PAIDOFF</td>\n      <td>300</td>\n      <td>7</td>\n      <td>9/9/2016</td>\n      <td>9/15/2016</td>\n      <td>35</td>\n      <td>Master or Above</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21</td>\n      <td>21</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/10/2016</td>\n      <td>10/9/2016</td>\n      <td>43</td>\n      <td>High School or Below</td>\n      <td>female</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>24</td>\n      <td>24</td>\n      <td>PAIDOFF</td>\n      <td>1000</td>\n      <td>30</td>\n      <td>9/10/2016</td>\n      <td>10/9/2016</td>\n      <td>26</td>\n      <td>college</td>\n      <td>male</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>35</td>\n      <td>35</td>\n      <td>PAIDOFF</td>\n      <td>800</td>\n      <td>15</td>\n      <td>9/11/2016</td>\n      <td>9/25/2016</td>\n      <td>29</td>\n      <td>Bechalor</td>\n      <td>male</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n0           1             1     PAIDOFF       1000     30       9/8/2016   \n1           5             5     PAIDOFF        300      7       9/9/2016   \n2          21            21     PAIDOFF       1000     30      9/10/2016   \n3          24            24     PAIDOFF       1000     30      9/10/2016   \n4          35            35     PAIDOFF        800     15      9/11/2016   \n\n    due_date  age             education  Gender  \n0  10/7/2016   50              Bechalor  female  \n1  9/15/2016   35       Master or Above    male  \n2  10/9/2016   43  High School or Below  female  \n3  10/9/2016   26               college    male  \n4  9/25/2016   29              Bechalor    male  "
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 69,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[ 0.49,  0.93,  3.06,  1.98, -1.3 ,  2.4 , -0.8 , -0.86],\n       [-3.56, -1.7 ,  0.53, -0.51,  0.77, -0.42, -0.8 , -0.86],\n       [ 0.49,  0.93,  1.88,  1.98,  0.77, -0.42,  1.25, -0.86],\n       [ 0.49,  0.93, -0.98, -0.51,  0.77, -0.42, -0.8 ,  1.16],\n       [-0.67, -0.79, -0.48, -0.51,  0.77,  2.4 , -0.8 , -0.86]])"
                    },
                    "execution_count": 69,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\ntest_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n\nFeature = test_df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(test_df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n\nX2 = Feature\ny2 = test_df['loan_status'].values\nX2 = preprocessing.StandardScaler().fit(X2).transform(X2)\n\nX2[0:5]"
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "(54, 8)\n(54,)\n"
                }
            ],
            "source": "print(X2.shape)\nprint(y2.shape)"
        },
        {
            "cell_type": "code",
            "execution_count": 70,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Avg F1-score: 0.63\nKNN Jaccard Score: 0.67\nAvg F1-score: 0.67\nDecision Tree Jaccard Score: 0.72\nAvg F1-score: 0.76\nSVM Jaccard score: 0.80\nLogLoss: : 0.57\nAvg F1-score: 0.6604\nLOG Jaccard score: 0.7407\n"
                },
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": "/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:635: DeprecationWarning: jaccard_similarity_score has been deprecated and replaced with jaccard_score. It will be removed in version 0.23. This implementation has surprising behavior for binary and multiclass classification tasks.\n  'and multiclass classification tasks.', DeprecationWarning)\n"
                }
            ],
            "source": "yhatKNN=neigh2.predict(X2)\nKNNJaccard = jaccard_similarity_score(y2, yhatKNN)\nKNNF1 = f1_score(y2, yhatKNN, average='weighted')\nprint(\"Avg F1-score: %.2f\" % KNNF1 )\nprint(\"KNN Jaccard Score: %.2f\" % KNNJaccard)\n\n\nyhatDEC = Tree.predict(X2)\nDTJaccard = jaccard_similarity_score(y2, yhatDEC)\nDTF1 = f1_score(y2, yhatDEC, average='weighted')\nprint(\"Avg F1-score: %.2f\" % DTF1 )\nprint(\"Decision Tree Jaccard Score: %.2f\" % DTJaccard)\n\nyhatSVM=clf.predict(X2)\nSVMJaccard = jaccard_similarity_score(y2, yhatSVM)\nSVMF1 = f1_score(y2, yhatSVM, average='weighted')\nprint(\"Avg F1-score: %.2f\" % SVMF1)\nprint(\"SVM Jaccard score: %.2f\" % SVMJaccard)\n\nyhatLOG = LogR.predict(X2)\nyhatLOGproba = LogR.predict_proba(X2)\nLogRJaccard = jaccard_similarity_score(y2, yhatLOG)\nLogRF1 = f1_score(y2, yhatLOG, average='weighted')\nLogloss = log_loss(y2, yhatLOGproba)\nprint(\"LogLoss: : %.2f\" % Logloss)\nprint(\"Avg F1-score: %.4f\" % LogRF1)\nprint(\"LOG Jaccard score: %.4f\" % LogRJaccard)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# Report\nYou should be able to report the accuracy of the built model using different evaluation metrics:"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Algorithm          | Jaccard | F1-score | LogLoss |\n|--------------------|---------|----------|---------|\n| KNN                | 0.63       | 0.67        | NA      |\n| Decision Tree      | 0.67       | 0.72        | NA      |\n| SVM                | 0.76       | 0.80        | NA      |\n| LogisticRegression | 0.74       | 0.66        | 0.57       |"
        },
        {
            "cell_type": "markdown",
            "metadata": {
                "button": false,
                "new_sheet": false,
                "run_control": {
                    "read_only": false
                }
            },
            "source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n\n<h3>Thanks for completing this lesson!</h3>\n\n<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients\u2019 ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n\n<hr>\n\n<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.6",
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        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
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            "file_extension": ".py",
            "mimetype": "text/x-python",
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            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
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